Indirect genetic effects: A key component of the genetic architecture of behaviour

Abstract

Behavioural ecology research increasingly focuses on why genetic behavioural variation can persist despite selection. Evolutionary theory predicts that directional selection leads to evolutionary change while depleting standing genetic variation. Nevertheless, evolutionary stasis may occur for traits involved in social interactions. This requires tight negative genetic correlations between direct genetic effects (DGEs) of an individual’s genes on its own phenotype and the indirect genetic effects (IGEs) it has on conspecifics, as this could diminish the amount of genetic variation available to selection to act upon. We tested this prediction using a pedigreed laboratory population of Mediterranean field crickets (Gryllus bimaculatus), in which both exploratory tendency and aggression are heritable. We found that genotypes predisposed to be aggressive (due to DGEs) strongly decreased aggressiveness in opponents (due to IGEs). As a consequence, the variance in total breeding values was reduced to almost zero, implying that IGEs indeed greatly contribute to the occurrence of evolutionary stasis. IGEs were further associated with genetic variation in a non-social behaviour: explorative genotypes elicited most aggression in opponents. These key findings imply that IGEs indeed represent an important overlooked mechanism that can impact evolutionary dynamics of traits under selection.

Full text

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Indirect genetic eects: a key
component of the genetic
architecture of behaviour
Francesca Santostefano1, Alastair J. Wilson2, Petri T. Niemelä3 & Niels J. Dingemanse1,3
Behavioural ecology research increasingly focuses on why genetic behavioural variation can persist
despite selection. Evolutionary theory predicts that directional selection leads to evolutionary change
while depleting standing genetic variation. Nevertheless, evolutionary stasis may occur for traits
involved in social interactions. This requires tight negative genetic correlations between direct genetic
eects (DGEs) of an individual’s genes on its own phenotype and the indirect genetic eects (IGEs) it
has on conspecics, as this could diminish the amount of genetic variation available to selection to
act upon. We tested this prediction using a pedigreed laboratory population of Mediterranean eld
crickets (Gryllus bimaculatus), in which both exploratory tendency and aggression are heritable. We
found that genotypes predisposed to be aggressive (due to DGEs) strongly decreased aggressiveness in
opponents (due to IGEs). As a consequence, the variance in total breeding values was reduced to almost
zero, implying that IGEs indeed greatly contribute to the occurrence of evolutionary stasis. IGEs were
further associated with genetic variation in a non-social behaviour: explorative genotypes elicited most
aggression in opponents. These key ndings imply that IGEs indeed represent an important overlooked
mechanism that can impact evolutionary dynamics of traits under selection.
Behavioural ecologists increasingly focus on studying the adaptive processes maintaining individual dierences
in behaviour within animal populations. Several adaptive explanations have been proposed for why selection
might maintain behavioural variation rather than erode it (reviewed by1–3). For example, frequency dependent
selection1, temporal and spatial heterogeneity4, 5, or life-history trade-os6–8 have all been implied to explain the
stable coexistence of dierent behavioural ‘types’ within populations. It is implicitly assumed that genes carried
by focal individuals contribute to behavioural dierences, such that directional selection should both erode var-
iance and cause a change (over generations) in mean phenotype9, 10. However, evolutionary theory also predicts
that evolutionary stasis may occur despite directional selection in the presence of ‘indirect genetic eects’ (IGEs)
generated by social interactions11–15. is key insight has largely been ignored in behavioural ecology theory
explaining individual variation in behaviour, despite the fact that many behavioural traits are expressed as part
of social interactions.
Quantitative genetic theory implies that social interactions can have major evolutionary repercussions, particu-
larly when an individual’s phenotype is aected by the genotypes of conspecics: these eects are called IGEs12, 13, 15.
IGEs can greatly inuence evolutionary processes when they are correlated with the direct genetic eects (DGEs)
of an individual’s genotype on its own phenotype. For example, in mussel cultures, individuals genetically pre-
disposed to grow quickly in competitive situations are also genetically predisposed to reduce growth in others by
depriving them of feeding opportunities16. e resulting negative genetic correlation between DGEs and IGEs can
impose major evolutionary constraints, by eectively reducing the amount of variation in total breeding value of a
trait within a population17–19. e presence of IGEs may thus lead to evolutionary stasis in the phenotype, imply-
ing that directional selection does not necessarily lead to evolutionary change. Interestingly, positive genetic cor-
relations between DGEs and IGEs are predicted to instead speed up the response to directional selection relative
to expectations from classic evolutionary theory (e.g. refs 14 and 15). For example, a positive covariance between
DGEs and IGEs on aggression in a study of deer mice (Peromyscus maniculatus) implies that this trait can evolve
1Research Group Evolutionary Ecology of Variation, Max Planck Institute for Ornithology, 82319, Seewiesen,
Germany. 2Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter,
Cornwall Campus, TR10 9EZ, Penryn, UK. 3Behavioral Ecology, Department of Biology, Ludwig-Maximilians-
University of Munich, 82152, Planegg-Martinsried, Germany. Correspondence and requests for materials should be
addressed to N.J.D. (email: n.dingemanse@lmu.de)
Received: 3 January 2017
Accepted: 10 July 2017
Published: xx xx xxxx
OPEN

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very rapidly14. is is because selection for increased aggression would drive the evolution of a social environment
in which aggression is more readily elicited by interacting conspecics. erefore, IGEs arising from social inter-
actions can both provide a source of additional genetic variation that either facilitates rapid selection responses or
serves as a source of evolutionary constraint on phenotypes20. However, to date IGEs have largely been ignored as
a potential mechanism explaining evolutionary stasis in individual behaviour research21–24.
IGEs are expected to exist on traits such as aggression and dominance11, i.e., traits that are expressed explic-
itly as part of social interactions. Interestingly, IGEs can also aect the evolution of other aspects of phenotype,
including behavioural traits not expressed within a social context, provided these covary genetically with traits
that do harbour IGEs25. For example, the literature on ‘behavioural syndromes’ oen reports that traits expressed
in social interactions (e.g., aggressiveness, sociability) are phenotypically correlated with other risky behaviours
expressed in non-social contexts, such as exploratory tendency, or anti-predator boldness (meta-analysis,26).
Of course, these correlations are important for evolutionary dynamics only if they are underpinned by genetic
processes1027. us, if IGEs are present for a social behaviour such as aggression, the evolution of any trait genet-
ically correlated either with the social behaviour or its IGEs may be aected.
Here, we investigated whether IGEs contribute to the genetic architecture of behavioural variation expressed
in social and non-social contexts. We repeatedly measured two behavioural traits (exploration, non-social, and
aggression, social) in a pedigreed laboratory population of Mediterranean eld crickets (Gryllus bimaculatus)
descended from wild-caught grandparents. For the data presented in this paper, we show elsewhere that explor-
atory behaviour and aggressiveness are both repeatable and heritable (subject to DGEs) but not genetically cor-
related (Santostefano et al. under review). Here we expand upon these analyses by quantifying (i) whether IGEs
also contributed to genetic variance in aggressiveness, (ii) whether, for aggressiveness, DGEs (tendency to act
aggressively) and IGEs (tendency to elicit aggressiveness) were correlated, and (iii) whether IGEs on aggression
were also correlated with DGEs for exploration, a trait not directly involved in social interactions. Our approach
thus implies that drawing evolutionary predictions while ignoring IGEs not only on the focal trait, but also on
other seemingly independent traits, can be greatly misleading.
Results
Sources of variation in single traits. Exploration behaviour was signicantly repeatable (r = 0.45) and
heritable (h2 = 0.28) (see also Santostefano et al. under review). Aggressiveness was also signicantly repeat-
able (rf = 0.17) and heritable (h2 = 0.05), while it additionally harboured a signicant opponent identity eect
(ro = 0.11) (see also Santostefano et al. under review; estimates re-printed in Table1). Here we expanded
upon these analyses by estimating IGEs on aggression and testing for their correlation with DGEs. Doing so,
demonstrated that this opponent eect harboured a small, but signicant, amount of genetic variation for focal
aggression (VIGE = 0.026, SE 0.017) (Model 6, Table1). In other words, there was genetic variation not just in
the tendency of individuals to be aggressive, but also in the level of aggressiveness they elicited in their social
partners. Furthermore, the genetic correlation between DGEs and IGEs for aggression was strong and negative
(rG = −0.83, SE 0.37) (Model 7, Table1). AIC model comparison to simpler models also provided strongest sup-
port for this nal model (Model 7, TableS1). In other words, individuals genetically predisposed towards express-
ing higher levels of aggression as a focal were also predisposed to suppress aggressiveness in their opponents. As
a consequence of this tight negative genetic correlation, the estimated total heritable variation in aggression (also
known in the literature as τ2 28) (VTBV/VTOT = 0.016, SE 0.030; where VTBV = VDGE + VIGE + 2COVDGE,IGE = 0.05
1 + 0.026 − 2*0.030 = 0.016; VTOT = 0.99) was considerably smaller (namely, 3.19 times) than what ‘traditional’
estimates of heritability based on DGEs would (inappropriately) conclude (h2 = 0.051, SE 0.024).
Model
Variance σ² (SE) Correlations r Foc-Opp (SE) Tes t
Focal Opponent
Residual PE G LogL Χ² df PPE(f) DGE PE(o) IGE
1 — — — — — — 0.98 (0.03) — — — −1168.01 — — —
20.17 (0.02) — — — — — 0.83 (0.02) — — — −1131.75 72.52 0/1 <0.01
30.17 (0.02) — — 0.11 (0.02) — — 0.71 (0.03) — — — −1116.89 29.27 0/1 <0.01
40.17 (0.02) — — 0.11 (0.02) — — 0.71 (0.03) −0.21 (0.11) — — −1115.34 3.1 1 0.08
5 — 0.12 (0.03) 0.05 (0.02) 0.11 (0.02) — — 0.71 (0.03) −0.19 (0.14) — — −1110.99 8.7 0/1 <0.05
6 — 0.12 (0.03) 0.05 (0.02) —0.08 (0.03) 0.03 (0.02) 0.71 (0.03) −0.20 (0.15) — — −1109.15 3.68 0/1 <0.05
7—0.12 (0.03) 0.05 (0.02) —0.08 (0.03) 0.03 (0.02) 0.71 (0.03) —0.01 (0.18) −0.83 (0.37) −1107.05 4.2 1*<0.05
Table 1. Results of the univariate mixed ‘animal model’ tted to partition variation in aggressive behaviour with
random intercepts for focal and opponent identity. Estimates of variance components and their correlations
are given with associated standard errors. Random eects are expressed as the proportion of total phenotypic
variation not attributable to xed eects explained by each eect. Focal and opponent variances, as well as their
covariance, are partitioned into environmental (PE) and genetic (G) components. For each model, variance
terms are provided with a likelihood ratio test (LRT) between the given model and the previous model, with
associated degrees of freedom (df) and values of P. e most parsimonious model (model 7) is denoted in bold
face. *tested in addition over an equal mix of df = 1 and df = 2 (representing a test of variance and covariance
together, against model 5), Χ² = 7.88, p < 0.05

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Among-trait correlations. Multivariate models corroborated the strong negative genetic correlation
between DGEs and IGEs on aggression (rG = −1.02, SE 0.40, P < 0.05) (Table2). We note this estimate is slightly
greater than that presented above (though based on SE the condence intervals will be strongly overlapping) and
very slightly outside the permissible parameter space for a (true) correlation (we also note that not constraining
the parameter space in the model t allows better convergence and an estimate of the uncertainty associated
with rG). However, the genetic correlation between DGEs on exploration and DGEs on aggression was close to
zero and non-signicant (rG = −0.04, SE 0.24, P > 0.05) (Table2), contrary to predictions from the behavioural
syndrome literature. Multivariate models also provided some evidence for a positive genetic correlation between
IGEs on aggression and DGEs expressed in the non-social trait of exploration, although the estimated was mar-
ginally non-signicant (rG = 0.59, SE 0.28, P = 0.056) (Table2).
Using the estimated G matrix, we compared the t of ve model structures (considered a priori) using AIC
(Table3, Fig.1). is approach is warranted because a multivariate rather than a pair-wise bivariate approach
greatly increases statistical power. A model where both the correlation between DGEs on exploration and IGEs
on aggression, as well as the correlation between DGEs and IGEs on aggression were included (Model 3) tted
the data best, consistent with our inferences from likelihood-based testing of the pairwise correlations (above)
(Table3, Fig.1). e direct genetic correlation between aggression and exploration was not included in this
model, consistent with this correlation being close to zero in the full model estimated above. is full pattern is
somewhat dicult to interpret since, given the magnitude of estimated correlations between IGEs for aggression
and DGEs on both behaviours, we might have expected a stronger (direct) genetic correlation between aggression
GAggressiveness (DGE) Exploration (DGE) Aggressiveness elicited (IGE)
Aggressiveness (DGE) —−0.04 (0.24) −1.02 (0.40)
Exploration (DGE) −0.01 (0.03) —0.59 (0.28)
Aggressiveness elicited (IGE) −0.04 (0.02) 0.05 (0.03) —
Table 2. Estimated additive genetic (G) covariances and correlations (with SE) between two behaviours
(aggression and exploration), and IGEs on aggression. We present covariances (lower-o diagonals) and
correlations (upper-o diagonals) for each set of traits. Correlations printed in bold-face are signicant
(P < 0.05) based on likelihood ratio tests derived from the multivariate model detailed in the main text.
Model ΔAIC Akaike Weight Relative LL
3. B = 0 0 0.78 1.00
4. C = 0 3.62 0.13 0.16
5. A, B, C estimated 5.49 0.05 0.06
1. A, B, C = 0 6.06 0.04 0.05
2. A = 0 8.64 0.01 0.01
Table 3. Relative t of ve multivariate models diering in architecture of genetic correlations between direct
genetic (DGE) and indirect genetic (IGE) eects based on the Akaike’s information criterion (AIC). We present
each model’s AIC-value relative to the model with the lowest AIC-value (ΔAIC), its weight, and relative
likelihood. Model denominations refer to Fig.1: A is the correlation between DGEs and IGEs on aggressiveness;
B is the correlation between DGEs on exploration and DGEs on aggressiveness; C is the correlation between
DGEs on exploration and IGEs on aggressiveness. Model 5 (the complete model) is presented in Table2.
Figure 1. Correlation structure of the ve hypothesized multivariate model structures presented in Table3
(detailed in the Methods). A is the correlation between DGEs and IGEs on aggressiveness; B is the correlation
between DGEs on exploration and DGEs on aggressiveness; C is the correlation between DGEs on exploration
and IGEs on aggressiveness. Estimated correlations with corresponding SEs derived from the full model (Model
5, presented in Table2) are shown with each arrow; bolded arrows represent paths with statistical support from
the LRT and AIC.

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and exploration. As this was not the case, it is possible that the IGEs and DGEs for aggression are not as tightly
correlated as implied by the point estimate (see also our discussion above). With this caveat noted, we nd by AIC
comparison that individuals with a high genetic merit for explorative tendency in novel environments tended
to elicit more aggression (Table3, Fig.1). Taken together with the strong (and signicant) genetic correlation
between DGEs and IGEs on aggression (Table1; Table2), we view this as evidence that the social environment
can indeed inuence the evolution of behaviours including those expressed outside the social context.
Discussion
is study investigated a largely overlooked mechanism, indirect genetic eects, which may contribute to the
observed behavioural variation in social traits under selection and impact their evolutionary dynamics. Our study
on male Mediterranean eld crickets conrmed that the phenotypic expression of aggression and exploration
was repeatable, and showed that the former depended on opponent, as well as focal identity. Both behaviours
harboured additive genetic variance, but—importantly—heritable variation in focal aggressiveness arose jointly
from the genotypes of the focals (DGEs) and opponents (IGEs) (Table1). As aggressiveness represents an impor-
tant component of an oen-documented “aggression-boldness syndrome”26, the evolutionary consequences of
these IGEs may extend to other associated traits. Indeed, we found evidence for a genetic architecture suggesting
that the evolution of a non-social trait such as exploration may not be independent from the evolution of a social
trait, and vice versa, given that its DGEs were correlated with the IGEs acting on aggression. Our study therefore
identies IGEs as an important overlooked component of the (multivariate) genetic architecture of behaviour that
should be considered when making predictions on the evolution of traits studied in ‘personality’ research. Our
results generally imply that IGEs can have consequences for the evolutionary trajectories of a wide range of traits,
including those not expressed as part of social interactions (e.g., exploratory tendency, body size, etc.).
e estimated magnitude of IGEs on aggression in this study was similar to that documented in other spe-
cies (e.g. refs 14 and 29). Crucially, we also found a strong negative correlation between DGEs and IGEs for this
interactive behaviour, a result that contrasts with positive correlations reported for agonistic behaviours in some
other species14 (but not all29). An important consequence of the strong negative covariance between direct and
indirect genetic eects is that the total heritable variation for aggressiveness is reduced17, 18. is is highlighted
in our results by the discrepancies between the (direct) heritability estimates (h2 aggression = 0.051), and the
total heritable variation for aggression including IGEs and their covariance with DGEs (τ2 = VTBV/VTOT, = 0.016).
While indirect eects (genetic and non-genetic component) clearly contribute to variance in focal aggressiveness,
the negative correlation between IGEs and DGEs means that the potential for evolution of the phenotypic mean
in response to directional selection is even lower than suggested by the (direct) heritability20, 30.
e sign of this correlation can also be interpreted in terms of behavioural feedback processes and the func-
tional role of aggression. For example, in species (or contexts) where individuals escalate agonistic behaviour
through positive feedbacks (i.e. aggression elicits aggression14) direct-indirect (genetic) covariance will be posi-
tive. Conversely, negative correlations arise when aggression is asymmetric, being directed by more competitive
(or dominant) individuals towards subordinate social partners. is is because, in a dyadic contest, a geno-
type predisposing to contest winning by the focal will necessarily predispose to losing when encountered in an
opponent202831, 32. us, the negative genetic correlation found here actually suggests that, at least within the con-
text of the behavioural trials conducted, aggression is being used to assert social dominance in this species. e
importance of such correlations applies to any species displaying aggressive interactions, regardless of whether
aggression is part of stereotyped escalated context or linked to dominance.
A question not previously considered is whether IGE on aggression (or indeed other social traits) will also
have evolutionary implications for non-social aspects of ‘animal personality’23. For example, traits such as bold-
ness and exploratory tendency are oen correlated with aggression (e.g. mediated by proximate mechanisms
such as variation in metabolism7, 8), leading to the suggestion of an integrated ‘aggression-boldness syndrome’
(meta-analysis, ref. 26). When we thus extended our analysis to include a non-social behaviour, we found evi-
dence of a genetic covariance structure that would preclude independent evolution of exploration and aggres-
siveness. Interestingly, this was manifest as a correlation between IGEs on aggression and DGEs for exploration,
rather than the conventional (i.e. direct additive) genetic covariance structure that is normally estimated in
studies seeking to understand multivariate selection responses (e.g. using the Lande equation, refs 15 and 33).
Specically, a high genetic merit for exploration is associated with a tendency to elicit more aggressive behav-
iour from conspecific partners (Table2, Fig.1). The correlation between DGEs in exploration and IGEs in
aggression mirrors, at the genetic level, conclusions of a phenotypic study on the closely related cricket species
G. campestris24. In this species we found a positive correlation between individual (phenotypic) merits for explo-
ration and aggression elicited in conspecics (rI = 0.45, SE 0.17) (Note the corresponding among-individual
phenotypic correlation estimated in the present experiment is also signicantly positive and similar in magni-
tude: rI = 0.37, SE 0.09; TableS2). us, had we not considered IGEs, we would incorrectly have concluded that
exploratory behaviour and aggressiveness were evolutionarily independent10, 34. Instead we expect that selection
on exploratory behaviour will cause correlated evolution of the social environment with consequences for mean
aggression (and vice versa). However, it does not follow that the IGEs constraining evolution of mean aggression
will necessarily constrain the evolution of exploration behaviour too. In general, IGEs arising from competition
related processes are expected to impose constraints on traits that are consequent, rather than causal to, contest
outcomes (and thus resource acquisition35), a scenario that is not clearly the case here. We fully acknowledge
that our study is not directly informative for the causal pathways linking aggression to exploration, but several
possibilities can be hypothesised. For example, the positive association could arise if exploration in a novel envi-
ronment increases the likelihood of encountering rivals (and thereby provoking more attacks from conspecics).
Exploration could also be favoured in individuals eliciting aggression as a result of competition for territories in

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the population. Alternatively, exploratory tendency may be (genetically) correlated with other traits that directly
mediate agonistic behaviour in competitive interactions (e.g. size, weapon morphology).
We also note that the variance partitioning approach used to model IGEs in this paper is mathematically
equivalent to the alternative (but complementary) ‘trait based’ approach advocated by others15, 18, 36. In this latter
framework, an interaction eect coecient ψ (‘psi’), captures the eect of a measured conspecic trait (or traits)
on focal phenotype. ψ represents a standardized reaction norm slope, hence the level of phenotypic plasticity to a
social environmental gradient11. In the context of our study, ψ is captured by the correlation between DGEs and
IGEs: individuals responded to the aggressiveness and explorative tendency expressed by social partners (because
IGEs on aggression are correlated to DGEs of both behaviours), implying that ψ is multivariate in nature. A hot
question in quantitative genetics revolves around the issue of whether genotypes dier in their responsiveness to
phenotypes of conspecics, which would imply heritable variation in ψ37, 38. An interesting follow-up question is
thus whether responsiveness to other individuals (ψ) varies according to behavioural ‘types’, as has recently been
suggested in the personality literature23, 39. Importantly, a genetic architecture that includes genetic variation in ψ
and its covariance with other DGEs and IGEs would likely reveal further interesting repercussions for evolution-
ary processes of behavioural traits.
In conclusion, a crucial consequence of social interactions is that they generate IGEs that not only contribute
to the observed variance but also impact evolutionary dynamics of traits under selection. In this case, constraints
on the phenotypic evolution of mean aggression arise from the negative correlation of direct and indirect genetic
eects. More generally, we note that the role of IGEs has received little attention in ‘animal personality’ research,
despite their potential implications for generating (and possibly maintaining) among-individual behavioural dif-
ferences. e merit of our approach is that by including IGEs into behavioural ecology’s existing ecological frame-
works to study ‘personality’, we may nally start fully integrating distinct areas of evolutionary biology such as
quantitative genetics and behavioural ecology23, 40. Doing so allows us to address outstanding questions about the
evolution of behaviour. Importantly, this heuristic framework may be broadly applied to any trait associated with
traits involved in social interactions. Indeed, traits such as coloration, ornaments, badge of status, are oen corre-
lated with aggression or dominance30. More generally, our study also demonstrates the importance of viewing the
phenotype (or genotype) from a multivariate perspective. at is, predictions of how ‘personality’ traits respond
to selection can be profoundly misleading if eects of social interactions mediated by IGEs are not considered.
Methods
Cricket collection, breeding, and housing. e parental generation of crickets was collected from a
tomato eld of approximately 2500 m2 near Capalbio, Italy (42°42′46.7′ N 11°33′99.3′ E) in July 2013. We collected
a total of 100 individuals: 34 adult males, 33 adult females, 12 near-nal instar males, and 21 near-nal instar
females. Following capture, crickets were transported to a climate controlled chamber at the Ludwig Maximilians
University of Munich (Planegg-Martinsried, Germany), where they were housed at 26 °C ( ± 0.5) and 65% ( ± 0.5)
humidity, under a 14:10 light:dark photoperiod (h) that wild crickets experienced at the time of capture.
Sexually mature wild-caught individuals from the parental generation were randomly paired 4 days aer
arrival in the laboratory. A total of 35 males and 35 females produced a total of 34 clutches from which ospring
hatched. We raised 40 ospring (F1) per parental pair (1360 ospring in total), from which we randomly selected
breeders once reaching adulthood. We adopted a full-sib/half-sib breeding design41 for the F1 and F2 generations
by having each male fertilize the clutches of two females. We used a total of 35 males and 70 females from the F1
generation, and 15 males and 30 females from the F2 generation. is resulted in 47 F2 and 21 F3 viable full-sib
families. Details on the breeding and rearing protocol are provided in the Supplementary Material.
Adult males of the F2 and F3 generation were subjected to repeated behavioural assays. e study focused on
males only because aggression through escalated stereotyped ghts is largely male-limited, thus more dicult to
measure in females. e number of available adult ospring (of both sexes) per female was n = 622 for the F2 and
n = 281 for the F3 (per female mean ± SD: 8.64 ± 2.46 for the F2 and 5.51 ± 2.44 for the F3). Of these, a total of
455 males were selected and screened for behavioural phenotypes (335 from the F2 and 120 from the F3).
Experimental protocol. Behavioural trials were conducted between January and June 2014. Each individ-
ual was repeatedly assayed for each of 2 behaviours on the same day (exploration and aggression, described in
detail below) following24; the same individual was assayed for each behaviour 6 times, with measurements taken
approximately one week apart (range 7–9 days). Because individual identication is required for the aggression
test (detailed below), subjects were marked with coloured tape on the pronotum (red or blue, randomly assigned
each time) the day before a focal trial (see also24). e two tests were always done sequentially and in the same
order; carry-over eects could therefore not be modelled. We chose this set-up because it ensured that all indi-
viduals were given the exact same treatment since this greatly facilitates comparison between individuals42, 43.
e 455 males were divided into 7 groups of 40 individuals (F2), one group of 55 individuals (F2), and 3 groups
of 40 individuals (F3). 15 individuals of the F2 were only tested twice, because they were subsequently used for
other purposes. Individuals were divided into groups according to their estimated age (days post-moulting) to
avoid any possible age-related eects on aggression (see also24). All individuals within a group were tested on the
same day (8 individuals simultaneously), randomized for time of the day and test location. Dyads of males paired
for the aggression tests were randomly assigned amongst the non-related individuals within the same group to
produce social environments that were homogenous with respect to relatedness.
All trials were performed on a rack tted with two shelves, each equipped with a camera, in the same climate
room where the individuals were housed (detailed in ref. 24. All trials were recorded using high-resolution digital
video cameras (Basler GenICam, Germany) tted 43 cm above each testing arena. e cameras were connected to
a computer outside of the climate room and managed using the soware MediaRecorder (Noldus, Netherlands).
Videos were recorded at 27.81 frames per second and 1600 × 1200 pixels resolution.

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A small number of trials were excluded from the nal dataset: 31 of 1888 (F2) and 3 of 608 (F3) for explora-
tion trials (respectively 1.64% and 0.49%), and 27 of 944 (F2) and 5 of 304 (F3) for aggression trials (respectively
2.86% and 1.64%) due to technical problems with data recording or video-tracking. Note that the total number of
aggression trials is approximately half of that of other trials since two individuals are involved in each aggression
test. e nal sample size (behavioural tests) was therefore 2462 for exploration (mean number per individual:
5.27, SD 1.23) and 1195 for aggression (mean number per individual: 5.16, SD 1.28) tests.
Behavioural trials and scoring. Exploration and aggression behaviour were assayed following the protocol
in24 (for an illustration of the setup, see Fig. 2 in24). Briey, at the onset of the exploration test, each individual
was moved (inside its own shelter) from its home container to the exploration arena. Exploration activity was
then recorded automatically for 30 minutes. Following the exploration test, the shelters were removed and the
individuals given a further 10 minutes to acclimatize. e divider between two arenas was then lied, aer which
we lmed each dyad engaging in social interactions for a period of 10 minutes. We then returned the crickets to
their home containers in the allotted housing slots within the climate room.
Exploration and aggression videos were analysed using Ethovision version 11.0 (Noldus, the Netherlands).
is soware package enables tracking of isolated individuals and extracts the spatial coordinates for each video
frame. We summed up all distances to calculate the total distance moved in the novel environment (exploration
test), viewed as proxy for ‘exploration behaviour’ (following44). For the aggression test, we calculated the total
time each individual spent moving towards the opponent (‘relative movement’ for simplicity), by summing up
only the consecutive samples (frames) where the relative distance between subjects decreased (see User man-
ual of Ethovision v11.0, Noldus Information Technology 2014, for details). We set a maximum interaction dis-
tance between the two subjects of 8 cm based on pilot trials to dene a range in which the directional movement
towards the other cricket would be meaningful. We validated the choice of the variable ‘relative movement’ for
aggression both for a related cricket species and for a subset of the current dataset. Relative movement was highly
correlated with the variable ‘approach’ towards the opponent that we scored manually, and is commonly used in
aggression tests44. e choice and validation of relative movement as a measure for aggression is detailed in the
Supplementary Material.
Quantitative genetics analysis. Univariate models. We conducted two sets of statistical analyses.
First, using univariate mixed-eects models we partitioned the total phenotypic variance (VP) for each meas-
ured trait (aggression, exploration) into its underlying components: residual within-individual variance (VR)
and among-individual variance (VI(f)) for the focal individual. e latter component represents the statistical
signature of “personality” variation45 and so was tested in its own right before we further partitioned it in another
model into direct (additive) genetic (VDGE) and permanent environmental (VPE(f)) eects. For aggression, we also
estimated the variance explained by the opponent identity (VI(o)), which was, in turn, also split into its environ-
mental (VPE(o)) and genetic (VIGE) components. Partitioning of genetic from non-genetic focal (direct) and, for
aggressiveness, opponent (indirect) variances was done using a univariate mixed-eects “animal” model46 that
utilised the (additive) relatedness matrix determined from the pedigree. Covariance between direct and indirect
eects was modelled in both genetic and permanent environment parts of the model. Behavioural data was avail-
able for both partners in every dyadic aggression trial, meaning the designations of focal and opponent within a
dyad are arbitrary. us, for a two individuals in a dyad (i, j), we model the indirect eect of j on i’s phenotype and
vice versa (i.e. each dyad contributes two focal records). We note that a possible issue arises since residuals are
likely to be correlated between the two observations per dyad, but since the correlation is likely negative where
aggression reects dominance, this is not readily accounted for by modelling a random eect of dyad. We there-
fore blocked the data le into two “realizations” of focal versus opponent designation, each block containing focal
records on one individual within each dyad. e two data blocks were then analysed simultaneously within a sin-
gle mixed model formulation, with no cross-block covariance terms tted, but under an imposed constraint that
within-block (co)variance components to be estimated are equal in the two data blocks. More detail and ASReml
code to implement this modelling strategy is provided in the Supplementary material.
To statistically control for sources of variation in behaviours not directly relevant to our hypotheses, we
included the following xed eects: test sequence (covariate, range 1–6, mean centered), generation (F2 or F3)
and clutch number (rst or second) (both coded as −0.5 and 0.5, following47). All models were tted using
restricted maximum likelihood; dependent variables were mean-centred and variance standardized to facilitate
comparison of variance components across traits. roughout, we assumed a Gaussian error distribution, which
was conrmed for all response variables aer visual inspection of model residuals.
Adjusted individual repeatability48 was estimated for each behavioural trait by calculating the proportion of
the total phenotypic variance not attributable to xed eects that was explained by among-individual variance
(i.e., where VI(f) = VPE(f) + VDGE). For aggression, we estimated both focal and opponent repeatabilities. Direct
heritability (h2), indirect genetic eects (IGEs), and the proportional contribution of VPE(f) (pe(f)2) and VPE(o)
(pe(o)2) relative to the total phenotypic variance were estimated as each variance component divided by total phe-
notypic variance not attributable to xed eects. From this latter model, we further calculated the variance in total
breeding value (VTBV) for aggression. VTBV allows estimating the total heritable variation for this trait available
to selection, taking into account DGEs, IGEs, and their genetic covariance. VTBV was calculated following28 (eq.
6, for a group size of two interacting individuals, n = 2) as VTBV = VDGE + VIGE + 2COVDGE,IGE. We calculated the
total heritable variation for aggression as τ2 = VTBV/VTOT28.
Multivariate models. As a next step, we used a multivariate extension of the framework described above to
estimate patterns of between-trait (aggression, exploration) covariance at the among-individual (I) level, further

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partitioned into the permanent environmental and genetic levels by respectively estimating the PE and G matri-
ces. is allowed us to estimate the correlation between the opponent identity eect on aggressiveness and the
focal identity eect on exploration (I matrix), and to partition it into its genetic and environmental components.
We tted exploration and aggression as response variables and included only xed eects that explained signi-
cant variation in univariate analyses (detailed above).
Signicance testing in mixed-eects models. We tested model xed eects using conditional F-tests with denom-
inator degrees of freedom (df) estimated from the algebraic algorithm in ASReml 4.149. We used a hierarchical
stepwise forward approach50, 51 to evaluate the statistical signicance of random eects by likelihood ratio tests
(LRTs). We started with a phenotypic model that contained only xed eects and residual variation (Model 1). We
then tested for dierences among individuals in the focals (Model 2) and the opponents (Model 3) by sequentially
tting individual and opponent identities respectively. Model 4 tested for the phenotypic correlation between the
two. We repeated the same structure when testing for genetic variation and added DGEs (Model 5), IGEs (model
6), and their correlation (model 7). We assumed a χ2-distribution for the test statistic which is calculated as
twice the dierence in log-likelihood between a model where a target random eect was tted versus not tted52.
Variances are bound to be positive, therefore in testing them we applied the LRT assuming (for testing a single
variance component) an equal mixture of χ20 and χ21 53–55.
For multivariate models, we compared the t of a model where all covariances at a specic level were esti-
mated with one where those covariances were instead all constrained to zero (with degrees of freedom equal to
the number of covariance terms). is provides an overall (i.e. matrix level) test for nonzero covariance structure.
We further tested the signicance of each covariance separately by applying a LRT (assuming χ21) as described
above. is led to 5 alternative multivariate models, diering in the correlation structure (See Table3 for details).
We also compared the t of the alternative models (both for univariate and multivariate analyses separately)
using the Akaike information criterion (AIC)56, 57, calculating ΔAIC relative to the model with the lowest AIC.
We calculated the Akaike weight and model likelihood for each model58 using the package ‘qpcR’59 in R 3.1.060.
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Acknowledgements
We thank Giovanni Casazza for providing access to the eld site, Yvonne Cämmerer and Bettina Rinjes for help in
maintaining the crickets, and Vivek H. Shridar, Patricia Velado Lobato, and Simone Ariens for help in performing
the experiments. We are grateful to Alexia Mouchet for help in constructing our database, and members of the
Research Group “Evolutionary Ecology of Variation” for feedback and discussion. We thank Dave Westneat,
Pierre-Olivier Montiglio, and two anonymous reviewers for comments on a previous version of the manuscript.
F.S. and N.J.D. were supported by the Max Planck Society; F.S. was supported by the International Max Planck
Research School for Organismal Biology; PTN was supported by the Alexander von Humboldt foundation (3.3-
7121-FIN/1151177) and A.J.W. was supported by a BBSRC David Phillips Research Fellowship (BB/L022656/1).
Author Contributions
F.S. and N.J.D. conceived the study. F.S. collected the data. F.S. analysed the data with input from N.J.D., A.J.W.,
and P.T.N. F.S. wrote the rst dra of the manuscript. All authors contributed to revisions and approved the most
recent version of the manuscript.
Additional Information
Supplementary information accompanies this paper at doi:10.1038/s41598-017-08258-6

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